Surface reflectance estimation from spatio-temporal subband statistics of moving object videos

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buir.advisorOnural, Levent
dc.contributor.authorKülçe, Onur
dc.date.accessioned2016-01-08T18:19:35Z
dc.date.available2016-01-08T18:19:35Z
dc.date.issued2012
dc.descriptionAnkara : The Department of Electrical and Electronics Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2012.en_US
dc.descriptionIncludes bibliographical refences.en_US
dc.description.abstractImage motion can convey a broad range of object properties including 3D structure (structure from motion, SfM), animacy (biological motion), and its material. Our understanding of how the visual system may estimate complex properties such as surface reflectance or object rigidity from image motion is still limited. In order to reveal the neural mechanisms underlying surface material understanding, a natural point to begin with is to study the output of filters that mimic response properties of low level visual neurons to different classes of moving textures, such as patches of shiny and matte surfaces. To this end we designed spatio-temporal bandpass filters whose frequency response is the second order derivative of the Gaussian function. Those filters are generated towards eight orientations in three scales in the frequency domain. We computed responses of these filters to dynamic specular and matte textures. Specifically, we assessed the statistics of the resultant filter output histograms and calculated the mean, standard deviation, skewness and kurtosis of those histograms. We found that there were substantial differences in standard deviation and skewness of specular and matte texture subband histograms. To formally test whether these simple measurements can in fact predict surface material from image motion we developed a computer-assisted classifier based on these statistics. The results of the classification showed that, 75% of all movies are classified correctly, where the correct classification rate of shiny object movies is around 77% and the correct classification rate of matte object movies is around 71%. Next, we synthesized dynamic textures which resembled the subband statistics of videos of moving shiny and matte objects. Interestingly the appearance of these synthesized textures were neither shiny nor matte. Taken together our results indicate that there are differences in the spatio-temporal subband statistics of image motion generated by rotating matte and specular objects. While these differences may be utilized by the human brain during the perceptual process, our results on the synthesized textures suggest that the statistics may not be sufficient to judge the material qualities of an object.en_US
dc.description.provenanceMade available in DSpace on 2016-01-08T18:19:35Z (GMT). No. of bitstreams: 1 0006239.pdf: 5335478 bytes, checksum: 287b7abf0d6deb4167c18cb0d0ff9547 (MD5)en
dc.description.statementofresponsibilityKülçe, Onuren_US
dc.format.extentxiv, 104 leaves, illustrationsen_US
dc.identifier.urihttp://hdl.handle.net/11693/15504
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectThe Human Visual Systemen_US
dc.subjectSurface Reflectanceen_US
dc.subjectMovie Subband Statisticsen_US
dc.subjectThree-Dimensional Second Order Derivative of Gaussian Filteren_US
dc.subjectTexture Synthesisen_US
dc.subjectSteerable Pyramiden_US
dc.subject.lccTA1637 .K85 2012en_US
dc.subject.lcshImage processing--Digital techniques.en_US
dc.subject.lcshDigital video.en_US
dc.subject.lcshThree-dimensional display systems.en_US
dc.subject.lcshSurfaces (Technology)en_US
dc.titleSurface reflectance estimation from spatio-temporal subband statistics of moving object videosen_US
dc.typeThesisen_US
thesis.degree.disciplineElectrical and Electronic Engineering
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

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